Data Teams...A Unified Management Model for Successful Data-Focused Teams

Book

Brief

Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project.

DataOps: Powerful Ideas

Brief

Book

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make DataOps investments work better. This DataOps All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth DataOps Self-Assessment. Featuring 700 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which DataOps improvements can be made.

DataOps Strategy A Complete Guide - 2020 Edition

Brief

Book

Why is database performance so critical now? How much overhead do you afford? Why is open source critical to data as a service? Who knows how big the files are, and modify time? Where is data as a service most useful? This one-of-a-kind DataOps Strategy self-assessment will make you the established DataOps Strategy domain standout by revealing just what you need to know to be fluent and ready for any DataOps Strategy challenge. How do I reduce the effort in the DataOps Strategy work to be done to get problems solved? How can I ensure that plans of action include every DataOps Strategy task and that every DataOps Strategy outcome is in place? How will I save time investigating strategic and tactical options and ensuring DataOps Strategy costs are low? How can I deliver tailored DataOps Strategy advice instantly with structured going-forward plans?

DataOps A Complete Guide - 2020 Edition

Brief

Book

How do you nurture data science capabilities? Have you upgraded your data analytics team to a DataOps team? What are the specific, high-impact data capabilities empowered by Intelligent SDN and related technologies? Who receives training on ICT, data standards or data analytics? How will you use qualitative data analysis to improve? This premium DataOps self-assessment will make you the established DataOps domain specialist by revealing just what you need to know to be fluent and ready for any DataOps challenge.  How do I reduce the effort in the DataOps work to be done to get problems solved? How can I ensure that plans of action include every DataOps task and that every DataOps outcome is in place? How will I save time investigating strategic and tactical options and ensuring DataOps costs are low? How can I deliver tailored DataOps advice instantly with structured going-forward plans?

Practical DataOps...Delivering Agile Data Science at Scale

Brief

Book

This book shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output.